1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
2/10/2022 Electricidad 48087 Andrés atrasado del mes anterior
4/10/2022 Comida 41760 Andrés NA
4/10/2022 Comida 12860 Andrés NA
8/10/2022 Brussels 25300 Tami NA
8/10/2022 Comida 25300 Tami NA
10/10/2022 Comida 67895 Tami NA
11/10/2022 Enceres 11730 Andrés Ida easy
11/10/2022 Enceres 7146 Andrés Uber easy
15/10/2022 Tres toques 28600 Tami NA
17/10/2022 Comida 47140 Andrés NA
19/10/2022 Comida 28110 Andrés FREST verduras y frutas
23/10/2022 Comida 76701 Tami NA
26/10/2022 Comida 35941 Tami NA
26/10/2022 Enceres 11980 Andrés Mascarilla
27/10/2022 Comida 17536 Tami NA
30/10/2022 VTR 21990 Andrés entel
28/10/2022 Comida 27940 Andrés tres toques
3/11/2022 Diosi 56000 Tami Vacunas
4/11/2022 Electricidad 49266 Andrés Pac enel
6/11/2022 Comida 19325 Tami NA
8/11/2022 Agua 10092 Andrés NA
9/11/2022 Diosi 117980 Andrés 58990 por 2
9/11/2022 Comida 73462 Tami NA
9/11/2022 Diosi 17535 Tami Correa petsu
12/11/2022 Gas 76350 Andrés NA
12/11/2022 Enceres 16986 Andrés uber ida matri fran
14/11/2022 Comida 51263 Tami NA
19/11/2022 Comida 2943 Tami NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 4.7672e+08   2    5.2455 0.0056 ** 
## lag_depvar    7.7922e+10   1 1714.7877 <2e-16 ***
## Residuals     2.3402e+10 515                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   969.7489 13487.93 0.0187695
## 2-0 27332.951 21577.5892 33088.31 0.0000000
## 2-1 20104.113 16635.7831 23572.44 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
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## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   363 49567.21 15757.551
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2043.228037   4050.802689   -548.390892   2429.125898  -2990.037024 
##             7             8             9            10            11 
##    511.329294  -5666.753174  -1175.385446  -3952.417835   -391.212960 
##            12            13            14            15            16 
##  -4916.765882  -1570.403284   -860.980603    413.016048  -3215.618298 
##            17            18            19            20            21 
##   -342.383076  -2099.631114   6637.698849  -1530.531166  -1205.157418 
##            22            23            24            25            26 
##   1481.289224  -1190.221050    234.333899   1691.499192  -7114.584408 
##            27            28            29            30            31 
##    964.929930   8201.421098    388.916023    -43.470406  -2428.380484 
##            32            33            34            35            36 
##   1560.094525   4549.574182   1085.201431   2348.088147  -1918.206491 
##            37            38            39            40            41 
##   4569.982544   4281.377580  -2313.563186  -3004.914159  -1118.041780 
##            42            43            44            45            46 
## -10743.762786   7333.199813   2564.111208   1361.684362   8094.614714 
##            47            48            49            50            51 
##    642.015618   6487.087742   6649.833228  -5967.689323  -4844.953186 
##            52            53            54            55            56 
##  -5082.585708  -7926.998751   6165.020476  -4072.316914  -4873.275169 
##            57            58            59            60            61 
##   3895.001250    905.340420    -20.825494    152.032298  -4988.666682 
##            62            63            64            65            66 
##  18154.808685   3587.226109  -3708.564578   5886.306782   7284.407816 
##            67            68            69            70            71 
##  14555.221683   1557.296626 -13338.756935  -1359.573710   4602.382422 
##            72            73            74            75            76 
##  -4956.229828  -4432.430764 -10502.928101   2507.319882  -5374.972039 
##            77            78            79            80            81 
##   1108.716533  -6831.589830    606.830583  -2304.613991  -2640.075465 
##            82            83            84            85            86 
##  -3873.266667   -469.340516   2376.463598   3806.598737    498.814877 
##            87            88            89            90            91 
##   -467.682359    213.227660   4315.397075  -1170.063895   1149.527186 
##            92            93            94            95            96 
##  -2070.789957  -1041.061895    184.751481    280.102827  -7480.755828 
##            97            98            99           100           101 
##   2426.952220  -8582.203254  -2885.505405  -3978.488698  -1665.782297 
##           102           103           104           105           106 
##  -1191.684403   3247.373501  -2297.894032   2642.695801  -1127.265911 
##           107           108           109           110           111 
##   1002.580568   2610.805721  -3145.065541  -4701.331514   -810.848893 
##           112           113           114           115           116 
##   1941.568876  11718.403592  -1271.854169   2648.214510   4233.375790 
##           117           118           119           120           121 
##   3458.316318  -1154.002936  -4758.865732  -3740.837438   2321.263207 
##           122           123           124           125           126 
##  -1741.495745   1340.151755   8852.106319    803.172153     88.300423 
##           127           128           129           130           131 
##  -2558.744846   2633.187673   7021.784004    954.859430  -8554.212022 
##           132           133           134           135           136 
##   1738.114482   4118.108243  -3197.248529  -1435.201154   -861.440574 
##           137           138           139           140           141 
##  -3882.927568   1197.252780   -488.310754  -2905.301654   1737.968198 
##           142           143           144           145           146 
##  -1871.365912  -7812.765077   2087.818175  -3446.464515   2146.245620 
##           147           148           149           150           151 
##   -228.344511   1049.405169   -340.899877   1369.640137   1195.801616 
##           152           153           154           155           156 
##   3359.237233  -4874.247735  -1164.381287  -3222.045564   5982.660976 
##           157           158           159           160           161 
##   9743.233286  -3139.304301  -4477.213113   3918.518821    484.348524 
##           162           163           164           165           166 
##   2978.681135  -5645.419441  -6459.573411   4468.273473  17676.161084 
##           167           168           169           170           171 
##   3820.799964   -218.353663  -2257.850528   -899.335258   3803.524988 
##           172           173           174           175           176 
##    -30.361061  -7872.771358   3107.659571   4553.641134    832.826987 
##           177           178           179           180           181 
##   8957.017942  -9081.468274  -3253.263347 -10508.746440 -10954.378179 
##           182           183           184           185           186 
##   1566.623733   9605.690227  -1173.448550   6187.673310   6780.141305 
##           187           188           189           190           191 
##  13348.626254   8556.555589  -3971.847027   2587.938108  10487.062180 
##           192           193           194           195           196 
##  -1569.588029  -2348.518850 -10160.735120  -6182.064757   1449.511027 
##           197           198           199           200           201 
##  -5025.105907  -9561.295610   5665.508399  -2822.046583  -1454.584672 
##           202           203           204           205           206 
##   -543.083063   6753.353157  10099.449042    739.652149   3086.416045 
##           207           208           209           210           211 
##   3248.001448   5923.389222  12948.917918  -5630.210247 -11191.828395 
##           212           213           214           215           216 
##  -5490.426844 -10377.705137  -4809.442107   1813.061446 -12741.056597 
##           217           218           219           220           221 
##  16721.047036   8038.890865   1712.176978  26865.693355  12560.710110 
##           222           223           224           225           226 
##   7319.582438  13995.860526  -3993.971617  -1767.616250   3786.823811 
##           227           228           229           230           231 
##    372.223858   2779.944025   9045.413380   5845.300087  -1895.975442 
##           232           233           234           235           236 
##  -1782.146671   9501.843464 -11465.985112  -7159.848636  -8368.924869 
##           237           238           239           240           241 
##  -9879.834422   3351.103422   1601.526752  -8059.627006  -8711.158730 
##           242           243           244           245           246 
##   9412.948701  -7509.085243   2779.441196 -10032.586815  -3737.375636 
##           247           248           249           250           251 
##   1747.825617   1305.969262 -12030.425525   3985.564031   2367.977972 
##           252           253           254           255           256 
##   4493.219776   2382.956223   -930.450800  11371.342809  21045.885136 
##           257           258           259           260           261 
##   3245.341154  -4226.170655   4194.480988  -1621.254068   3833.174045 
##           262           263           264           265           266 
##  -4766.253766 -10768.820834  -4534.850884   -301.689700  -4968.908931 
##           267           268           269           270           271 
##   9023.037540  -4093.488231   4399.804942  -1924.644576   4624.645424 
##           272           273           274           275           276 
##    874.839190   7465.557724  -1291.528761  12159.482274  -4518.568608 
##           277           278           279           280           281 
##   1828.886898   -272.102439   7961.474384  -4988.336591  -2619.412722 
##           282           283           284           285           286 
## -11124.989261  -2457.859640  18879.938175   7887.867204   2797.519750 
##           287           288           289           290           291 
##   -570.224253    980.996392   6478.411564   6932.630394 -18751.805835 
##           292           293           294           295           296 
## -10979.224330  -7885.130122   9950.353784   3285.893755   -987.204200 
##           297           298           299           300           301 
##  27601.632999  10084.383974   4872.110657   9480.953654   2782.182224 
##           302           303           304           305           306 
##  -1095.948364   7868.414721 -24350.583703  -3397.840258     -5.932466 
##           307           308           309           310           311 
##  -6792.599432  -3744.640619   3185.401900  -8962.553008  -2938.559686 
##           312           313           314           315           316 
##  -7879.524064   1919.743158  -2821.793586   2387.157805  -3771.690961 
##           317           318           319           320           321 
##  27772.879062   -619.995537   3408.733837  10932.344829   5625.117416 
##           322           323           324           325           326 
##  32394.974976   4922.466457 -21114.684748   1805.808631   1133.839016 
##           327           328           329           330           331 
##  -6428.006712  -1631.777008 -33140.045004   1290.581976  -1913.194476 
##           332           333           334           335           336 
##    300.685236  -2785.999968   4478.340728    -87.664672  -6609.203137 
##           337           338           339           340           341 
##  -2731.105483  -1796.398317  -7282.301402   4290.610334   -980.463586 
##           342           343           344           345           346 
##  -1351.498150   -608.733480    555.287643    845.688306  -1270.166054 
##           347           348           349           350           351 
##  -9097.092168 -12802.084783   2797.955821  -3878.669949  -3202.974360 
##           352           353           354           355           356 
##  -5519.442843   2232.793242   1827.014156   3162.693432  -3397.541708 
##           357           358           359           360           361 
##   -134.298368   1046.783018   7362.787693    558.911904    236.284056 
##           362           363           364           365           366 
##   2853.271292  -2503.716121   -609.750434  -8470.801468  -4286.285734 
##           367           368           369           370           371 
##  -5844.733039  -4544.432273  -6824.281067   5481.538646    773.264062 
##           372           373           374           375           376 
##   7501.269062  -7326.785127  -1899.533601  -3018.219083  -2082.677863 
##           377           378           379           380           381 
## -12067.279124   2376.859374 -10199.610766   6194.457477   9760.681929 
##           382           383           384           385           386 
##   3454.750137  -2106.050591   1909.674325   7028.065234  11633.711486 
##           387           388           389           390           391 
##  -5673.164536  -5181.261010     69.111050   8789.696291   1970.358883 
##           392           393           394           395           396 
##  11367.793401  -9818.012978   2927.478211    848.757747    700.111527 
##           397           398           399           400           401 
##   -513.170514   -409.804674 -14323.125509   8816.684678   -958.554655 
##           402           403           404           405           406 
##  -1137.129469   7230.097563  -7742.123737  -1033.808708  -2256.896649 
##           407           408           409           410           411 
##  -5523.310271  -2516.296912  -3557.259853  -8371.761685   6577.852054 
##           412           413           414           415           416 
##   2013.468306  -7027.332019  -7294.356272  14668.066363   4119.253138 
##           417           418           419           420           421 
##   4752.284353  -7818.899734  -4457.248731  -2276.832400   3159.905402 
##           422           423           424           425           426 
## -13703.096361  -2370.968618  -8671.170891   3499.545849   7413.428418 
##           427           428           429           430           431 
##   6931.779757  -3699.675763  -3803.728453  -4376.910132  -1413.824417 
##           432           433           434           435           436 
##  -5333.564157  -6213.454811  -5497.836002   -912.953094   -380.242565 
##           437           438           439           440           441 
##  -4523.695905   3052.962398   5265.043264  -4691.186866  -1762.924459 
##           442           443           444           445           446 
##   1974.349809  -3467.989357   3224.274929  -6227.644320 -11715.525660 
##           447           448           449           450           451 
##  -4031.176118  10138.800932  -1642.841105   5146.735630  -5530.103156 
##           452           453           454           455           456 
##   -742.834185    760.798032   3388.276965 -11941.485614   3789.324781 
##           457           458           459           460           461 
##  -6325.087186   6940.913612   3361.463774   2822.772790  -3555.675262 
##           462           463           464           465           466 
##   2411.830193    289.355118   2086.753518   -244.688041   3630.373286 
##           467           468           469           470           471 
##  -2387.561455   6080.892488  -6713.068858  -2672.268467  -1889.101421 
##           472           473           474           475           476 
##  -4332.182544   3361.362897   8129.320689  -5753.587364   1798.862637 
##           477           478           479           480           481 
##  -5879.117433  -2496.043592   2376.693560 -12589.841663  -9318.640316 
##           482           483           484           485           486 
##   -706.052352    501.850895   -503.407315   -895.564922  -9146.547204 
##           487           488           489           490           491 
##  11590.951498   6618.846840   7742.620104  -5178.166283   5671.257929 
##           492           493           494           495           496 
##   9551.583021   6240.721559 -13324.609244 -10294.486830  -3083.391583 
##           497           498           499           500           501 
##   -727.895901   -147.096875  -7254.020716   1034.767351   4692.261191 
##           502           503           504           505           506 
##   5866.933045    967.627340    380.765146  -6940.383472    925.073347 
##           507           508           509           510           511 
##  -4704.436329   2210.604112   -942.169575  -7800.232131   -187.843109 
##           512           513           514           515           516 
##  -2269.206782   -174.268610   1736.530926  -9114.058740  -7320.387719 
##           517           518           519           520 
##  24773.971108  10100.001129   6077.936137  -5173.930427 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17226.06 20088.20 24364.53 24081.02 26446.75 23765.38 24485.47 19692.53 
##       10       11       12       13       14       15       16       17 
## 19427.70 16756.50 17538.05 14250.26 14301.69 14969.84 16675.33 14986.53 
##       18       19       20       21       22       23       24       25 
## 16026.63 15396.87 22516.53 21595.73 21072.85 22972.79 22295.24 22951.22 
##       26       27       28       29       30       31       32       33 
## 24806.87 18703.36 20438.58 28317.08 28375.04 28046.24 25663.19 27073.00 
##       34       35       36       37       38       39       40       41 
## 30936.23 31286.48 32703.06 30200.59 34161.62 37386.56 34427.20 31221.33 
##       42       43       44       45       46       47       48       49 
## 30063.05 20593.09 28151.32 30600.60 31695.53 38569.56 38061.48 42748.17 
##       50       51       52       53       54       55       56       57 
## 47006.69 39666.24 34206.16 29202.71 22311.12 28634.17 25196.85 21475.00 
##       58       59       60       61       62       63       64       65 
## 25906.52 27172.68 27471.25 27885.24 23734.48 40412.92 42266.56 37487.55 
##       66       67       68       69       70       71       72       73 
## 41716.59 46658.06 57382.27 55385.61 40551.29 38044.05 41077.80 35348.00 
##       74       75       76       77       78       79       80       81 
## 30776.36 21430.97 24649.26 20553.57 22650.59 17519.31 19545.33 18767.79 
##       82       83       84       85       86       87       88       89 
## 17790.41 15849.20 17133.68 20760.69 25201.61 26196.68 26221.77 26841.75 
##       90       91       92       93       94       95       96       97 
## 30988.49 29812.90 30817.50 28871.78 28067.39 28437.47 28846.18 22389.90 
##       98       99      100      101      102      103      104      105 
## 25420.77 18414.65 17264.77 15295.21 15596.54 16277.48 20773.61 19852.30 
##      106      107      108      109      110      111      112      113 
## 23381.84 23170.71 24855.62 27747.49 25232.47 21657.28 21934.15 24594.31 
##      114      115      116      117      118      119      120      121 
## 35515.85 33699.21 35546.34 38560.40 40526.57 38202.87 32996.69 29318.88 
##      122      123      124      125      126      127      128      129 
## 31412.64 29683.56 30871.32 38510.97 38151.56 37208.17 34055.24 35845.79 
##      130      131      132      133      134      135      136      137 
## 41272.00 40709.35 31864.89 33136.32 36342.82 32734.63 31113.44 30193.64 
##      138      139      140      141      142      143      144      145 
## 26732.60 28154.45 27922.87 25597.03 27632.08 26249.62 19818.18 22864.61 
##      146      147      148      149      150      151      152      153 
## 20679.90 23672.63 24215.45 25814.19 25997.22 27660.06 28967.62 32015.68 
##      154      155      156      157      158      159      160      161 
## 27462.10 26721.19 24263.62 30188.62 41159.73 39481.21 36832.34 41878.94 
##      162      163      164      165      166      167      168      169 
## 43294.89 46728.71 42170.86 37453.44 42907.12 59294.77 61518.50 59924.28 
##      170      171      172      173      174      175      176      177 
## 56733.34 55124.19 57840.93 56859.91 49111.63 51949.93 55712.17 55748.55 
##      178      179      180      181      182      183      184      185 
## 62914.75 53367.26 50101.18 40861.66 32356.66 35883.31 46039.73 45492.90 
##      186      187      188      189      190      191      192      193 
## 51476.86 57251.95 68091.44 73401.99 67063.63 67258.08 74365.45 70019.23 
##      194      195      196      197      198      199      200      201 
## 65518.59 54706.06 48704.92 50136.68 45708.30 37836.06 44294.48 42512.58 
##      202      203      204      205      206      207      208      209 
## 42148.65 42629.50 49459.12 58394.92 58022.58 59756.43 61420.90 65231.94 
##      210      211      212      213      214      215      216      217 
## 74748.07 66789.40 54916.57 49497.13 40446.30 37388.08 40518.06 30485.95 
##      218      219      220      221      222      223      224      225 
## 47548.39 54907.54 55814.16 78698.86 86233.13 88246.85 95877.97 86781.47 
##      226      227      228      229      230      231      232      233 
## 80748.46 80328.20 76960.63 76117.73 80879.56 82250.98 76657.29 71845.16 
##      234      235      236      237      238      239      240      241 
## 77528.41 64106.28 56101.07 48009.55 39577.18 43791.04 45955.06 39371.44 
##      242      243      244      245      246      247      248      249 
## 33017.91 43354.23 37570.99 41527.30 33750.66 32449.75 36124.17 38962.85 
##      250      251      252      253      254      255      256      257 
## 29744.29 35713.45 39534.78 44756.76 47489.31 46979.23 57334.11 74922.94 
##      258      259      260      261      262      263      264      265 
## 74737.03 68012.66 69502.25 65703.25 67156.97 60881.96 50100.42 46106.98 
##      266      267      268      269      270      271      272      273 
## 46317.48 42403.82 51254.06 47507.62 51676.07 49782.78 53871.45 54169.01 
##      274      275      276      277      278      279      280      281 
## 60217.96 57839.80 67563.43 61456.40 61667.53 60007.95 65780.91 59478.56 
##      282      283      284      285      286      287      288      289 
## 56024.42 45522.00 43910.35 61232.85 66791.91 67203.51 64607.58 63690.16 
##      290      291      292      293      294      295      296      297 
## 67712.08 71642.81 52539.80 42589.99 36569.65 46945.11 50203.92 49313.22 
##      298      299      300      301      302      303      304      305 
## 73636.33 79612.89 80284.05 84920.67 83109.81 78114.01 81599.01 56366.27 
##      306      307      308      309      310      311      312      313 
## 52607.79 52285.89 46043.50 43238.31 46860.55 39373.70 38089.10 32622.11 
##      314      315      316      317      318      319      320      321 
## 36426.51 35603.56 39455.12 37428.98 63350.57 61180.41 62812.51 70852.60 
##      322      323      324      325      326      327      328      329 
## 73252.45 98867.82 97236.97 72940.33 71731.88 70080.58 61990.06 59097.19 
##      330      331      332      333      334      335      336      337 
## 28887.85 32594.77 33036.60 35368.71 34706.09 40503.38 41584.63 36807.25 
##      338      339      340      341      342      343      344      345 
## 36017.54 36144.87 31439.25 37469.75 38136.64 38396.45 39276.86 41072.17 
##      346      347      348      349      350      351      352      353 
## 42903.74 42654.09 35561.66 26079.90 31452.67 30307.69 29895.59 27499.49 
##      354      355      356      357      358      359      360      361 
## 32202.99 35977.02 40464.11 38643.58 39910.50 42060.21 49494.37 50047.86 
##      362      363      364      365      366      367      368      369 
## 50250.59 52726.72 50196.89 49638.52 42245.00 39427.02 35583.86 33350.85 
##      370      371      372      373      374      375      376      377 
## 29387.89 36714.16 39013.16 46940.21 40880.11 40324.36 38853.96 38384.28 
##      378      379      380      381      382      383      384      385 
## 29203.85 33826.18 26841.26 35103.89 45491.39 49075.62 47339.90 49342.08 
##      386      387      388      389      390      391      392      393 
## 55595.00 65130.45 58305.98 52745.03 52472.30 59890.78 60416.92 69131.30 
##      394      395      396      397      398      399      400      401 
## 58179.52 59754.67 59312.46 58793.60 57272.52 56027.55 42716.32 51347.27 
##      402      403      404      405      406      407      408      409 
## 50342.42 49303.19 55738.27 48241.38 47548.90 45866.74 41521.15 40345.69 
##      410      411      412      413      414      415      416      417 
## 38399.33 32462.29 40376.67 43318.47 37962.64 33024.93 47975.18 51840.29 
##      418      419      420      421      422      423      424      425 
## 55790.33 48219.68 44523.55 43192.52 46797.95 35155.83 34883.60 29112.03 
##      426      427      428      429      430      431      432      433 
## 34731.43 43103.08 50031.68 46780.01 43833.20 40742.11 40629.71 37088.88 
##      434      435      436      437      438      439      440      441 
## 33206.84 30426.24 32010.67 33869.84 31863.89 36755.81 42994.19 39729.35 
##      442      443      444      445      446      447      448      449 
## 39433.79 42456.13 40331.01 44341.64 39563.38 30548.18 29379.48 40796.56 
##      450      451      452      453      454      455      456      457 
## 40476.41 46157.53 41770.55 42122.06 43751.15 47489.06 37309.68 42184.66 
##      458      459      460      461      462      463      464      465 
## 37583.66 45192.82 48731.51 51365.96 48078.17 50431.36 50633.96 52390.26 
##      466      467      468      469      470      471      472      473 
## 51885.20 54844.56 52158.68 57236.64 50460.84 48059.10 46637.75 43244.21 
##      474      475      476      477      478      479      480      481 
## 47020.25 54523.16 48920.57 50632.83 45394.04 43764.45 46612.41 35970.50 
##      482      483      484      485      486      487      488      489 
## 29497.91 31377.15 34088.12 35585.99 36556.98 30164.05 42760.72 49456.24 
##      490      491      492      493      494      495      496      497 
## 56322.74 51006.17 55864.85 63538.99 67370.61 53554.06 44081.96 42096.47 
##      498      499      500      501      502      503      504      505 
## 42421.38 43216.74 37674.23 40085.88 45415.50 51127.23 51840.66 51951.81 
##      506      507      508      509      510      511      512      513 
## 45620.36 46967.44 43206.82 45976.88 45640.80 39323.27 40460.35 39631.13 
##      514      515      516      517      518      519      520 
## 40742.61 43396.63 36198.82 31453.17 55469.43 63673.35 67345.64 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8476
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    5.245511  0.5881362    3.112268
## t2* 1714.787732 28.8909735  246.831846
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.589925       5.385518   11.48167
## 2    lag_depvar 1365.705161    1728.010464 2172.25665

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Nov 21 00:55:35 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Nov 21 00:55:43 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Nov 21 00:55:50 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Nov 21 00:55:57 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Nov 21 00:56:05 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Nov 21 00:56:12 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Nov 21 00:56:19 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Nov 21 00:56:27 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Nov 21 00:56:34 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Nov 21 00:56:41 2022
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua NA 5.4832 5.682818 7.184559
Comida NA 309.9132 314.267773 342.032206
Comunicaciones NA 0.0000 0.000000 0.000000
Electricidad NA 43.8829 36.050227 30.598824
Enceres NA 22.4051 18.257500 25.582618
Farmacia NA 2.1980 8.633318 10.540412
Gas/Bencina NA 45.5550 28.116091 24.283941
Diosi NA 14.5163 35.337136 35.966853
donaciones/regalos NA 0.0000 7.821909 8.079971
Electrodomésticos/ Mantención casa NA 4.7328 33.021273 24.396118
VTR NA 25.7900 22.133773 21.067882
Netflix NA 6.9259 6.982000 7.428559
Otros NA 3.7813 1.718773 1.112147
Total 0 485.1837 518.022591 538.274088
## Joining, by = "word"


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: 35 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1804, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2022-12-09 00:04:58 sería de: 35.506 pesos// Percentil 95% más alto proyectado: 38.720,43

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 34884.62 34876.56
Lo.80 34911.29 34897.34
Point.Forecast 35505.54 36589.34
Hi.80 37282.91 41179.92
Hi.95 38259.54 43610.02


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.3170  990.6885
## s.e.  0.1473   35.2789
## 
## sigma^2 = 27645:  log likelihood = -292.99
## AIC=591.99   AICc=592.57   BIC=597.41
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.3143    874.785   3.8838
## s.e.  0.1479    516.143  17.2520
## 
## sigma^2 = 28272:  log likelihood = -292.97
## AIC=593.94   AICc=594.94   BIC=601.16
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 676.2442 647.0860 684.8307
Lo.80 796.4056 766.0188 765.4038
Point.Forecast 1023.3958 990.6883 944.3703
Hi.80 1250.3860 1215.3577 1236.6942
Hi.95 1370.5474 1334.2906 1426.4585


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Andrés Tami
1 marzo_2019 68268 175533
2 abril_2019 55031 152640
3 mayo_2019 192219 152985
4 junio_2019 84961 291067
5 julio_2019 205893 241389


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.4      
##  [7] tidytext_0.3.4      DT_0.26             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.1       xts_0.12.2         
## [13] forecast_8.18       wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.0     tm_0.7-9            NLP_0.2-1          
## [19] tsibble_1.1.3       forcats_0.5.2       dplyr_1.0.10       
## [22] purrr_0.3.5         tidyr_1.2.1         tibble_3.1.8       
## [25] ggplot2_3.4.0       tidyverse_1.3.2     sjPlot_2.8.12      
## [28] lattice_0.20-45     gridExtra_2.3       plotrix_3.8-2      
## [31] sparklyr_1.7.8      httr_1.4.4          readxl_1.4.1       
## [34] zoo_1.8-11          stringr_1.4.1       stringi_1.7.8      
## [37] DataExplorer_0.8.2  data.table_1.14.6   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.8          readr_2.1.3        
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2          tidyselect_1.2.0    lme4_1.1-31        
##   [4] htmlwidgets_1.5.4   munsell_0.5.0       codetools_0.2-18   
##   [7] its.analysis_1.6.0  withr_2.5.0         colorspace_2.0-3   
##  [10] ggfortify_0.4.15    highr_0.9           knitr_1.41         
##  [13] uuid_1.1-0          rstudioapi_0.14     TTR_0.24.3         
##  [16] labeling_0.4.2      emmeans_1.8.2       slam_0.1-50        
##  [19] bit64_4.0.5         farver_2.1.1        datawizard_0.6.4   
##  [22] fBasics_4021.93     rprojroot_2.0.3     vctrs_0.5.0        
##  [25] generics_0.1.3      xfun_0.35           timechange_0.1.1   
##  [28] R6_2.5.1            bitops_1.0-7        cachem_1.0.6       
##  [31] assertthat_0.2.1    networkD3_0.4       vroom_1.6.0        
##  [34] nnet_7.3-16         googlesheets4_1.0.1 gtable_0.3.1       
##  [37] spatial_7.3-14      timeDate_4021.106   rlang_1.0.6        
##  [40] forge_0.2.0         systemfonts_1.0.4   splines_4.1.2      
##  [43] lazyeval_0.2.2      gargle_1.2.1        selectr_0.4-2      
##  [46] broom_1.0.1         yaml_2.3.6          abind_1.4-5        
##  [49] modelr_0.1.10       crosstalk_1.2.0     backports_1.4.1    
##  [52] quantmod_0.4.20     tokenizers_0.2.3    tools_4.1.2        
##  [55] ellipsis_0.3.2      gplots_3.1.3        jquerylib_0.1.4    
##  [58] Rcpp_1.0.9          base64enc_0.1-3     fracdiff_1.5-2     
##  [61] haven_2.5.1         fs_1.5.2            magrittr_2.0.3     
##  [64] timeSeries_4021.105 lmtest_0.9-40       reprex_2.0.2       
##  [67] googledrive_2.0.0   mvtnorm_1.1-3       sjmisc_2.8.9       
##  [70] hms_1.1.2           evaluate_0.18       xtable_1.8-4       
##  [73] sjstats_0.18.2      ggeffects_1.1.4     compiler_4.1.2     
##  [76] KernSmooth_2.23-20  crayon_1.5.2        minqa_1.2.5        
##  [79] htmltools_0.5.3     tzdb_0.3.0          lubridate_1.9.0    
##  [82] DBI_1.1.3           sjlabelled_1.2.0    dbplyr_2.2.1       
##  [85] MASS_7.3-54         boot_1.3-28         Matrix_1.5-3       
##  [88] car_3.1-1           cli_3.4.1           quadprog_1.5-8     
##  [91] parallel_4.1.2      insight_0.18.7      igraph_1.3.5       
##  [94] pkgconfig_2.0.3     xml2_1.3.3          bslib_0.4.1        
##  [97] estimability_1.4.1  anytime_0.3.9       snakecase_0.11.0   
## [100] janeaustenr_1.0.0   digest_0.6.30       janitor_2.1.0      
## [103] rmarkdown_2.18      cellranger_1.1.0    curl_4.3.3         
## [106] gtools_3.9.3        urca_1.3-3          nloptr_2.0.3       
## [109] lifecycle_1.0.3     nlme_3.1-153        jsonlite_1.8.3     
## [112] tseries_0.10-52     carData_3.0-5       viridisLite_0.4.1  
## [115] fansi_1.0.3         pillar_1.8.1        fastmap_1.1.0      
## [118] glue_1.6.2          bayestestR_0.13.0   bit_4.0.5          
## [121] sass_0.4.2          performance_0.10.0  r2d3_0.2.6         
## [124] caTools_1.18.2
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))